Fracture Risk Assessment in the 2023 Osteoporosis Canada Guideline
Bibliographic record
Abstract
Radiologists and other diagnostic imaging specialists play a pivotal role in the management of osteoporosis, a highly prevalent condition of reduced bone strength and increased fracture risk. Bone mineral density (BMD) measurement with dual-energy X-ray absorptiometry (DXA) is a critical component of identifying individuals at high risk for fracture. Strategies to prevent fractures are consolidated in the Osteoporosis Canada clinical practice guideline which was updated in 2023. In this guideline, treatment recommendations are based upon a consideration of fracture history, 10-year major osteoporotic fracture (MOF) risk, and BMD T-score in conjunction with age. The current review aims to familiarize radiologists and other diagnostic imaging specialists with the reporting requirements needed to support implementation of this guideline using the FRAX™ risk calculation tool. Fortunately, for specialists already familiar with the Canadian Association of Radiologists and Osteoporosis Canada (CAROC) tool, the transition to FRAX-based reporting is readily accommodated in a radiology workflow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".